Place, temporality, and wine identity: a visual analysis of winery website imagery in the Okanagan valley wine region
Bibliographic record
Abstract
Over the past few decades there has been major growth and transformation in the global wine industry, resulting in increased competition between wineries and wine regions. In this context, many wineries have sought to construct brand identities connected to terroir, tradition, culture, and heritage to distinguish themselves. In the past 30 years, the Okanagan region of British Columbia, Canada has made a name for itself as a New World wine region through its production of high-quality wine and exceptional wine tourism experiences, however little is known about the specific branding strategies of its wineries. Through an analysis of photographic images found on local winery websites, this study reveals how visual representations of place and temporality provide a recurring motif across a range of wineries. More specifically, Okanagan wineries rely on wider region-specific imaginaries to construct their identity, and attempt to evoke longer-term temporalities through imagery suggestive of both historical and intergenerational connection to place. Drawing on the literature on corporate heritage, this study shows how these ‘omni-temporal’ practices are used strategically by wineries to create authenticity in a bid to appeal to consumers influenced by wine’s conventional place-time imaginaries. Overall, these findings add to the wider literature on wine brand identity and have relevance for industry and regional development stakeholders interested in place- and time-based branding strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".